In the pursuit of constructing truly autonomous mobile robots, the simultaneous localization and mapping problem is considered as one of the most important problems. Simultaneous localization and mapping (SLAM) is the process by which a mobile robot can build a map of an unknown environment and at the same time use this map to compute its location. SLAM is considered as a chicken-egg problem as to identify a robots location the map is needed which the robot is still building. One classical solution to the
SLAM problem is the EKF-SLAM method. EKF-SLAM uses Kalman filter, which is an efficient recursive filter that estimates the internal state of a linear dynamic system from a series of noisy measurements. Goal of this project is to implement and simulate the EKF-SLAM method for solving the SLAM problem.